用AI生成更打动人的房产文案，还能保证真实准确
AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting
- 构建三模块智能体，结合房源事实与用户偏好生成文案
- 人类测试显示，AI文案比专家写得更受欢迎且同样真实
- 适合需要批量生成精准营销内容的地产公司
本文提出一种基于大语言模型的智能体框架，用于自动化房产营销文案生成，实现内容与用户偏好对齐并突出关键事实特征。该智能体包含三个核心模块：(1) 基于市场价值预测的定位模块，识别可售特征；(2) 个性化模块，匹配用户偏好；(3) 营销模块，确保事实准确性与本地化信息完整。我们在潜在购房者群体中开展系统性人机实验，结果表明，该方法生成的营销描述在偏好度上显著优于人类专家撰写的内容，同时保持同等事实准确性。研究验证了该智能体在大规模精准文案生成中的有效性，为自动化内容创作提供了可信路径。
原文摘要 · Abstract (English)
This paper develops an agentic framework that employs large language models (LLMs) for grounded persuasive language generation in automated copywriting, with real estate marketing as a focal application. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin while maintaining the same level of factual accuracy. Our findings suggest a promising agentic approach to automate large-scale targeted copywriting while ensuring factuality of content generation.
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